What is Descriptive Analytics: Definition and Working

Descriptive analytics is crucial for businesses globally as it involves interpreting historical data to understand overall business circumstances. The rise of data and demand for data-driven decision-making has made descriptive analytics a foundation for advanced analytics. It involves finding trends, patterns, and insights to draw findings from data.

We will guide you through the basics of descriptive analytics to analyze data effectively and efficiently. It can unlock the potential of data, and improve overall business performance.

Table of Content

• What is Descriptive Analytics?
• Examples of Descriptive Analytics
• How Does Descriptive Analytics Work?
• Benefits of Descriptive Analytics
• Limitations of Descriptive Analytics  
• Descriptive Vs. Predictive Vs. Prescriptive Analytics
• Conclusion

What is Descriptive Analytics?

Descriptive analytics definition states that it is a branch of data analytics that involves looking into past data to understand what has happened and find relative patterns and trends. This analytics uses graphs, charts, and reports to make it easy to analyze. This is the preliminary phase of data processing, which involves using data to describe previous events.

Descriptive analytics is the process used to answer questions such as “What happened?” and “What is happening?”.

Examples of Descriptive Analytics

Below are the few descriptive analytics examples that are commonly used in the real-life scenarios:

• E-commerce- In e-commerce, descriptive analytics can be used to assess customer information, such as browsing behaviors, past purchases, and wish list selections, find valuable customers, and determine the most popular promotions and their effectiveness. This helps e-commerce businesses simplify their marketing and product strategies to increase sales.
• Healthcare- Descriptive analytics can be helpful in the healthcare industry to examine varied patient data, and find trends for better diagnoses, treatment histories, and outcomes. It helps medical practitioners and health care providers with respect to enhanced treatments and reducing medical expenses.

How Does Descriptive Analytics Work?

Descriptive analytics focuses on describing and analyzing historical data. By analyzing past performance, companies can identify the areas where they need to improve. This process involves using statistical methods as well as descriptive analytics tools for examining infinite data sets and extracting valuable information.

Here are several steps followed when conducting a descriptive analysis for a business:

• Track business metrics- Business metrics are crucial for identifying an organization’s progress toward achieving the overall business objective. You can compare these metrics with the previously set business goals, for attaining increased revenue, profit margin, inventory turnover, or better conversion rate.
• Collect the data- As you identified the data from various sources according to the business needs,  the next step is to collect suitable data. The data you plan to look at must be accurately organized and prepared for analysis during this phase. This may include structured data from various sources such as databases and spreadsheets. It may be found in multiple sources, including texts and images. So, it is crucial to gather your necessary data and organize it consistently.
• Prepare the data- This step is crucial for ensuring the data’s accuracy. Make sure the data is complete and well-organized before you begin analyzing it. It involves looking for errors, duplications, or any missing data. It will help to transform the data into a suitable format to be easy to work with.
• Analyze the data- Analyzing data is one of the essential steps in descriptive analytics. It includes exploring data and assuring consistency and accuracy while transforming it as needed, performing statistical analysis, and finally, interpreting the results. Analyzing the data allows you to identify patterns and trends, calculate central tendency and dispersion measures, and get conclusions that can help you make decisions.
• Present the data- After you finish all the mentioned analysis steps for your business, the remaining step is to present the data. You need to share the discovered information with the relevant stakeholders through data and information visualization, including charts, graphs, and tables for a better understanding of data-driven insights. Data visualization makes it simple for stakeholders to understand patterns and trends derived from the data analysis.

To display data effectively and accurately, it is essential to carefully review both the audience and the information that needs to be presented.

Benefits of Descriptive Analytics

Descriptive analytics can benefit various organizations or individuals, such as businesses, financial analysts, marketing professionals, etc. It is because they rely on a large amount of data to make profitable choices for the company.

Below are some of the benefits of descriptive analytics:

• By analyzing customer information, descriptive analytics can offer insights into customer behavior, choices, and requirements. Developing new products or services, improving customer service, and creating targeted marketing efforts can all benefit from this
• Descriptive analytics allows businesses to make data-driven decisions based on real-world understandings. This helps organizations reduce risk and improve their overall performance.
• Businesses can identify weak areas and improve operations by analyzing operational data. This can help organizations reduce costs, improve productivity, and increase profitability.
• Advanced data analysis methods like predictive analytics are built on descriptive analytics. Businesses can predict future occurrences and, thus, take early action to take advantage of opportunities and reduce risks by analyzing historical trends and patterns.

Limitations of Descriptive Analytics

Despite its value in comprehending historical data and trends, descriptive analytics comes with several limitations that organizations should take into consideration:

• Lack of Causality: Descriptive analytics provides insights into what happened and historical trends but doesn’t explain why certain patterns occurred. It cannot establish causal relationships between variables, which may limit its ability to predict future outcomes accurately.
• Limited Predictive Power: While descriptive analytics helps in understanding past performance, it doesn’t have the capability to forecast future events or anticipate changes. For more robust predictions, organizations need to employ predictive or prescriptive analytics methods.
• Static and Historical View: Descriptive analytics focuses on historical data, offering a static snapshot of the past. It may not account for dynamic changes or evolving trends, making it less effective in rapidly changing environments.
• Data Quality Dependency: The accuracy and reliability of descriptive analytics heavily rely on the quality of the underlying data. Inaccurate or incomplete data can lead to misleading conclusions and hamper the overall effectiveness of the analysis. Ensuring data quality is crucial for obtaining meaningful insights.

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Descriptive vs. Predictive vs. Prescriptive Analytics

Among the several types of Data analytics, descriptive, predictive, and prescriptive analytics are differentiated below.

Descriptive Analytics 

• Descriptive analysis is the simplest form of analysis that summarizes historical data that provides insights into what happened in the past.
• It’s all about explaining an organization’s past performance and identifying trends and patterns that can inform future decisions.
• Descriptive analysis is considered useful in understanding organizational performance and better identifying areas needing attention.

Predictive Analytics

• Predictive analytics uses statistical models and machine learning algorithms to analyze past data and predict future events or trends. 
• It is a data analysis method that uses past data to identify patterns and relationships that can be used to predict future outcomes. 
• Such analysis helps identify trends. Predict future events and provide information for decision making.

Prescriptive Analytics 

• It goes beyond descriptive and predictive analysis to recommend specific actions that can achieve a given outcome.
• Prescriptive analysis combines historical data. Predictive analytics and decision rules To recommend actions that are likely to achieve the desired results.
• It helps identify the best course of action to take in any given situation.

Conclusion

In summary, descriptive analysis is one of the most powerful tools to gain insights into historical data and past trends in the market. Using data visualization and statistical analysis techniques Descriptive analysis can help reveal marketing strategies, trends, and patterns, things that might otherwise be hidden.The potential of descriptive analytics to understand improvements and customer behavior with historical data should be addressed because in many industries data remains an essential part of any organization. Expertise in descriptive analysis has become essential for professionals to uncover new opportunities and innovations.